Abstract
Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus.
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Kit, C., Liu, X., Webster, J.J. (2006). Abbreviation Recognition with MaxEnt Model. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_14
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DOI: https://doi.org/10.1007/11671299_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
Online ISBN: 978-3-540-32206-1
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